System

Abstract

An object of the system according to the embodiment is to efficiently extract, organize, and share knowledge and experience of a specialist.SOLUTION: A system includes an interview part, a knowledge arrangement part, and a sharing part. The interview unit interviews the specialist and extracts knowledge and experience thereof. The knowledge organization unit organizes the knowledge and experience extracted by the interview unit. The sharing unit shares the knowledge and experience organized by the knowledge organization unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately extract, organize, and share the knowledge and experience of specialists efficiently, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently extract, organize, and share the knowledge and experience of specialists. [Means for solving the problem]

[0006] The system according to the embodiment includes an interview unit, a knowledge organization unit, and a sharing unit. The interview unit interviews specialists to elicit their knowledge and experience. The knowledge organization unit organizes the knowledge and experience elicited by the interview unit. The sharing unit shares the knowledge and experience organized by the knowledge organization unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently extract, organize, and share the knowledge and experience of specialists. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A media platform according to an embodiment of the present invention is a system for organizing, sharing, and spreading the knowledge and experience of specialists. In this system, a generative AI conducts interviews, extracts knowledge and experience, organizes it, and shares it. This allows the media platform to effectively organize, share, and spread the knowledge and experience of specialists.

[0029] A media platform according to an embodiment includes an interview unit, a knowledge organization unit, and a sharing unit. The interview unit interviews specialists to extract their knowledge and experience. For example, a generation AI asks questions to the specialists to extract their deep knowledge and experience. The interview unit can also analyze the specialists' past achievements and statements to create a customized list of questions. For example, the generation AI analyzes the specialists' past papers and books to extract important themes and topics. The knowledge organization unit organizes the knowledge and experience extracted by the interview unit. For example, the generation AI converts the interview content into text and edits it to make the specialized knowledge easier for the general public to understand. The knowledge organization unit can also automatically tag the interview content and link it to other related content. For example, the generation AI analyzes the interview content and automatically tags important keywords and phrases. The sharing unit shares the knowledge and experience organized by the knowledge organization unit. For example, the generation AI converts the interview content into video or audio format and publishes it on a media platform. The sharing unit can also use generative AI to analyze a specialist's past achievements and statements, and create text that highlights that person's passion and efforts. For example, generative AI can analyze a specialist's past achievements and statements and create text that highlights that person's passion and efforts. This allows the media platform to effectively organize, share, and spread the specialist's knowledge and experience.

[0030] The interview department can analyze the specialist's past achievements and statements before the interview and create a customized list of questions. For example, in the interview department, the generation AI analyzes the specialist's past papers and books to extract important themes and topics. Based on that information, it creates a customized list of questions to dig deeper in the interview. The interview department also analyzes the specialist's past lectures and interview videos to identify frequently mentioned keywords and phrases. Based on this, it generates a list of questions to elicit deeper insights. In the interview department, the generation AI analyzes the specialist's social media posts and blog articles to identify topics of interest and recent activities. Based on this, it creates a customized list of questions to dig deeper in the interview. This makes it possible to elicit deep insights based on the specialist's past achievements.

[0031] The interview section can automatically generate follow-up questions after the interview to further dig deeper into the specialist's knowledge. For example, after the interview is completed, the generation AI analyzes the interview content and identifies any unresolved questions or topics that require further digging. Based on this, follow-up questions are automatically generated and sent to the specialist. In addition, in the interview section, the generation AI summarizes the interview content and extracts important points and newly emerged questions. Based on this, follow-up questions are created to elicit even deeper insights. In addition, in the interview section, the generation AI analyzes the specialist's answers after the interview and automatically generates follow-up questions that request related additional information and specific examples. This allows for deeper digging into knowledge. This makes it possible to elicit even deeper insights after the interview.

[0032] The interview section can refer to the specialist's past interviews and lectures to generate questions from different perspectives. For example, in the interview section, the generation AI analyzes the specialist's past interview and lecture data to identify topics that have already been covered. Based on this, questions from different perspectives or new angles are automatically generated. The interview section also references the specialist's past statements and achievements to generate questions that dig deeper into existing knowledge. For example, questions are created that ask for rebuttals or clarification of past statements. The interview section also uses the generation AI to analyze the content of the specialist's past interviews and lectures to generate questions that address unresolved questions or provide new perspectives. This increases the depth of the interview. This allows questions from different perspectives to elicit more multifaceted insights.

[0033] The interview section translates the interview content in real time, allowing specialists from different language areas to be interviewed simultaneously. In the interview section, for example, the generation AI translates the interview content in real time, allowing specialists from different language areas to be interviewed simultaneously. For example, an interview conducted in English can be instantly translated into Japanese or French. The interview section also uses the generation AI to translate in real time during the interview, allowing specialists from different language areas to participate simultaneously. This makes it possible to conduct multilingual interviews. The interview section also uses the generation AI to translate the interview content in real time, allowing specialists from different language areas to be asked questions simultaneously. This enables knowledge sharing from an international perspective. This makes it possible to conduct interviews with specialists from different language areas simultaneously.

[0034] The knowledge organization unit can build a knowledge network by automatically tagging interview content and linking it to other related content. For example, the knowledge organization unit uses a generation AI to analyze interview content and automatically tag important keywords and phrases. This links it to other related content and builds a knowledge network. The knowledge organization unit also builds a system that automatically tags interview content and links it to related articles and videos. For example, it associates it with other interviews and materials on the same topic. The knowledge organization unit also builds a knowledge network by using a generation AI to tag interview content and link it to other related content. This allows users to easily access related information. Building a knowledge network allows users to easily access related information.

[0035] The knowledge organization unit can organize the interview content in chronological order and visually display the evolution of the specialist's knowledge. In the knowledge organization unit, for example, the generation AI organizes the interview content in chronological order and visually displays the evolution of the specialist's knowledge. For example, important events and discoveries by year are displayed in timeline format. The knowledge organization unit also organizes the interview content in chronological order and builds a system that visually displays the evolution of the specialist's career and knowledge. For example, this is visualized using graphs and charts. In addition, the knowledge organization unit organizes the interview content in chronological order and visually displays the evolution of the specialist's knowledge. This allows the user to understand the specialist's growth process at a glance. By visually displaying the evolution of the specialist's knowledge, the growth process can be understood at a glance.

[0036] The knowledge organization unit can convert interview content into different media formats and share it on multiple platforms. For example, the generation AI in the knowledge organization unit converts interview content into text, audio, or video formats and shares it on multiple platforms. For example, it can publish it as a blog post, podcast, or YouTube video. The knowledge organization unit can also build a system that converts interview content into different media formats and shares it on social media and media platforms. For example, it can distribute text as an article and audio as a podcast. The knowledge organization unit can also build a system that converts interview content into text, audio, or video formats and shares it on multiple platforms. This allows information to be delivered to different user groups. By sharing it in different media formats, information can be delivered to different user groups.

[0037] The knowledge organization unit can summarize the interview content and share it in a form that is easy to understand for specialists in different fields of expertise. For example, the knowledge organization unit uses a generation AI to summarize the interview content and share it in a form that is easy to understand for specialists in different fields of expertise. For example, technical terms may be replaced with simpler terms or diagrams may be used. The knowledge organization unit also builds a system in which the generation AI summarizes the interview content and shares it in a form that is easy to understand for specialists in different fields of expertise. For example, the main points may be organized in bullet points. The knowledge organization unit also uses a generation AI to summarize the interview content and share it in a form that is easy to understand for specialists in different fields of expertise. This promotes knowledge sharing across different fields. This makes it possible to share information in a form that is easy to understand for specialists in different fields.

[0038] The sharing unit uses a generative AI to analyze a specialist's past achievements and statements, and create text that highlights that person's passion and effort. For example, the sharing unit uses a generative AI to analyze a specialist's past achievements and statements, and create text that highlights that person's passion and effort. For example, it could highlight important projects and achievements. The sharing unit also uses a generative AI to analyze a specialist's past achievements and statements, and create text that highlights that person's passion and effort. This helps to increase the specialist's confidence. The sharing unit also uses a generative AI to analyze a specialist's past achievements and statements, and create text that highlights that person's passion and effort. For example, it could describe in detail the process of many years of research and development. This helps to increase the specialist's confidence by creating text that highlights their passion and effort.

[0039] The sharing unit can generate infographics that visually express the specialist's passion based on the interview content. In the sharing unit, for example, the generation AI generates infographics that visually express the specialist's passion based on the interview content. For example, research results or project progress may be shown in graphs and charts. In addition, the sharing unit also creates infographics that visually express the specialist's passion based on the interview content. This provides information in a visually easy-to-understand format. In addition, the sharing unit also has the generation AI analyze the interview content and generate infographics that visually express the specialist's passion. For example, important data and statistics may be visualized. In this way, by visually expressing the specialist's passion, information can be provided in an easy-to-understand format.

[0040] The sharing section can use the generative AI to compare the passion of a specialist with experts in other fields to identify commonalities and differences. For example, the generative AI can compare the passion of a specialist with experts in other fields to identify commonalities and differences. For example, it can compare the sources of passion and motivations in different fields. The sharing section can also compare the passion of a specialist with experts in other fields to identify commonalities and differences. This allows for the sharing of knowledge and experience in different fields. The generative AI can also compare the passion of a specialist with experts in other fields to identify commonalities and differences. For example, it can compare the success factors and challenges in different fields. This allows for the comparison with experts in different fields to identify commonalities and differences.

[0041] The sharing unit can create guidelines for other users to find their own passions based on the specialist's passions. In the sharing unit, for example, the generation AI creates guidelines for other users to find their own passions based on the specialist's passions. For example, it provides steps and hints for finding passions. In addition, the sharing unit has the generation AI analyze the specialist's passions and create guidelines for other users to find their own passions. This makes it easier for users to discover their own passions. In addition, the sharing unit has the generation AI create guidelines for other users to find their own passions based on the specialist's passions. For example, it shows how to choose and work in a field that you are passionate about. This makes it possible to provide guidelines for other users to find their own passions.

[0042] The sharing unit uses a generation AI to analyze the user's past behavioral data and suggest the most relatable hobbies. For example, the sharing unit uses a generation AI to analyze the user's past behavioral data and suggest the most relatable hobbies. For example, it recommends hobbies based on past search history and browsing history. The sharing unit also uses a generation AI to analyze the user's past behavioral data and suggest the most relatable hobbies. This makes it easier for users to discover new hobbies. The sharing unit also uses a generation AI to analyze the user's past behavioral data and suggest the most relatable hobbies. For example, it recommends hobbies based on past purchase history and events attended. This makes it possible to suggest the most relatable hobbies based on the user's past behavioral data.

[0043] The sharing unit can generate a step-by-step guide for the user to start a new hobby based on the interview content. In the sharing unit, for example, the generation AI generates a step-by-step guide for the user to start a new hobby based on the interview content. For example, it provides detailed explanations of the necessary tools and first steps. In addition, the sharing unit creates a step-by-step guide for the user to start a new hobby based on the interview content. This allows the user to easily start a new hobby. In addition, the sharing unit analyzes the interview content and generates a step-by-step guide for the user to start a new hobby. For example, it provides advice and tips for beginners. This allows the user to be provided with specific guidelines for starting a new hobby.

[0044] The sharing section uses the generating AI to compare hobbies from different cultural spheres and provide the user with a new perspective. For example, the generating AI in the sharing section analyzes hobbies from different cultural spheres and provides the user with a new perspective. For example, it introduces traditional hobbies and activities from different countries or regions. The sharing section also uses the generating AI to compare hobbies from different cultural spheres and provides the user with a new perspective. This allows the user to become interested in hobbies from different cultures. The sharing section also uses the generating AI to analyze hobbies from different cultural spheres and provides the user with a new perspective. For example, it introduces the history and background of hobbies from different cultures. This allows the user to be provided with a new perspective by comparing hobbies from different cultural spheres.

[0045] The sharing unit can use the generation AI to automatically create a community for starting a new hobby based on the user's interests and encourage participation. For example, the generation AI automatically creates a community for starting a new hobby based on the user's interests. For example, it generates an online forum or a social networking group. The sharing unit can also use the generation AI to automatically create a community for users to start a new hobby and encourage participation. This allows users to interact with people who have the same hobby. The sharing unit can also use the generation AI to automatically create a community for starting a new hobby based on the user's interests and encourage participation. For example, it can suggest events or workshops related to the hobby. This allows it to automatically create a community for users to start a new hobby and encourage participation.

[0046] The sharing department can use the generating AI to analyze the impact of the specialist's passion on the economy based on the interview content and provide specific data. For example, the sharing department uses the generating AI to analyze the interview content and analyze the impact of the specialist's passion on the economy. For example, it can provide data such as the market size and growth rate of a specific field. The sharing department can also use the generating AI to analyze the impact of the specialist's passion on the economy based on the interview content and provide specific data. This makes the economic impact visible. The sharing department can also use the generating AI to analyze the interview content and analyze the impact of the specialist's passion on the economy. For example, it can provide data such as job creation and investment amounts in related industries. This makes it possible to provide specific data on the impact of the specialist's passion on the economy.

[0047] The sharing unit can use the generation AI to predict demand for related products and services based on the interview content and create an economic development scenario. For example, the sharing unit has the generation AI analyze the interview content and predict demand for related products and services. For example, it predicts market demand for a new product based on specific technology or knowledge. The sharing unit also has the generation AI analyze the interview content and predict demand for related products and services and create an economic development scenario. This allows for specific proposals to companies and investors. The sharing unit also has the generation AI analyze the interview content and predict demand for related products and services. For example, it predicts market demand for products related to a new hobby or activity and creates an economic development scenario. This allows for demand forecasts for related products and services and create an economic development scenario.

[0048] The sharing unit uses the generative AI to compare the passions of specialists in different fields and identify common elements that contribute to economic development. For example, the generative AI analyzes the passions of specialists in different fields and identifies common elements that contribute to economic development. For example, it extracts elements of innovation and creativity. The sharing unit also uses the generative AI to compare the passions of specialists in different fields and identify common elements that contribute to economic development. This allows knowledge and experience from different fields to be shared. The generative AI also analyzes the passions of specialists in different fields and identifies common elements that contribute to economic development. For example, it extracts elements of sustainable development and social impact. This allows common elements that contribute to economic development to be identified by comparing the passions of specialists in different fields.

[0049] The sharing department can use generative AI to propose new business models that contribute to economic development based on the interview content. For example, the sharing department will use generative AI to analyze the interview content and propose new business models that contribute to economic development. For example, it will propose new services and products that utilize the knowledge and experience of specialists. The sharing department will also use generative AI to propose new business models that contribute to economic development based on the interview content. This allows for specific proposals to be made to companies and investors. The sharing department will also use generative AI to analyze the interview content and propose new business models that contribute to economic development. For example, it will propose new markets and business opportunities that utilize the passion of specialists. This allows for the proposal of new business models that contribute to economic development.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The interview department can also introduce interactive quiz-style questions to draw out specialists' knowledge and experience. For example, a generative AI can ask quiz-style questions to specialists and customize the next questions based on their answers. The interview department can also generate quizzes to test specialists' knowledge and adjust the direction of the interview based on the results. Furthermore, the interview department can analyze the specialists' responses when answering quizzes to identify topics that they are particularly interested in. This makes interviews more interactive and effective.

[0052] The interview department can analyze the specialist's past achievements and statements before the interview to create a customized list of questions. For example, the generative AI analyzes the specialist's past papers and books to extract important themes and topics. Based on that information, it creates a customized list of questions to dig deeper during the interview. The interview department can also analyze the specialist's past lectures and interview videos to identify frequently mentioned keywords and phrases. Based on this, it generates a list of questions to elicit deeper insights. The interview department can also use the generative AI to analyze the specialist's social media posts and blog articles to identify topics of interest and recent activities. Based on this, it creates a customized list of questions to dig deeper during the interview. This makes it possible to elicit deep insights based on the specialist's past achievements.

[0053] The interview department can automatically generate follow-up questions after the interview to further dig deeper into the specialist's knowledge. For example, after the interview is completed, the generation AI analyzes the interview content and identifies any unresolved questions or topics that require further digging. Based on this, follow-up questions are automatically generated and sent to the specialist. In addition, in the interview department, the generation AI summarizes the interview content and extracts important points and newly emerged questions. Based on this, follow-up questions are created to elicit even deeper insights. In addition, in the interview department, the generation AI analyzes the specialist's answers after the interview and automatically generates follow-up questions that request related additional information and specific examples. This allows for deeper digging into knowledge. This makes it possible to elicit even deeper insights after the interview.

[0054] The interview section can refer to the specialist's past interviews and lectures to generate questions from different perspectives. For example, the generation AI analyzes the specialist's past interview and lecture data to identify topics that have already been covered. Based on this, questions from different perspectives or new angles are automatically generated. The interview section also references the specialist's past statements and achievements to generate questions that dig deeper into existing knowledge. For example, it creates questions that ask for rebuttals or clarification of past statements. The interview section also uses the generation AI to analyze the content of the specialist's past interviews and lectures to generate questions that address unresolved questions or provide new perspectives. This increases the depth of the interview. This allows questions from different perspectives to elicit more multifaceted insights.

[0055] The interview section translates the interview content in real time, allowing specialists from different language areas to be interviewed simultaneously. For example, the generation AI translates the interview content in real time, allowing specialists from different language areas to be interviewed simultaneously. For example, an interview conducted in English can be instantly translated into Japanese or French. The interview section also uses the generation AI to translate in real time during the interview, allowing specialists from different language areas to participate simultaneously. This makes multilingual interviews possible. The interview section also uses the generation AI to translate the interview content in real time, allowing specialists from different language areas to be asked questions simultaneously. This enables knowledge sharing from an international perspective. This makes it possible to interview specialists from different language areas simultaneously.

[0056] The knowledge organization unit can build a knowledge network by automatically tagging interview content and linking it to other related content. For example, the generation AI analyzes interview content and automatically tags important keywords and phrases. This links it to other related content and builds a knowledge network. The knowledge organization unit also builds a system that automatically tags interview content and links it to related articles and videos. For example, it associates it with other interviews and materials on the same topic. The knowledge organization unit also builds a knowledge network by having the generation AI tag interview content and link it to other related content. This allows users to easily access related information. Building a knowledge network allows users to easily access related information.

[0057] The knowledge organization unit can organize the interview content in chronological order and visually display the evolution of the specialist's knowledge. For example, the generation AI organizes the interview content in chronological order and visually displays the evolution of the specialist's knowledge. For example, important events and discoveries by year are displayed in timeline format. The knowledge organization unit also organizes the interview content in chronological order and builds a system that visually displays the evolution of the specialist's career and knowledge. For example, this is visualized using graphs and charts. The knowledge organization unit also organizes the interview content in chronological order and visually displays the evolution of the specialist's knowledge. This allows the user to understand the specialist's growth process at a glance. By visually displaying the evolution of the specialist's knowledge, the growth process can be understood at a glance.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The interview department interviews the specialist to draw out their knowledge and experience. For example, the generation AI asks the specialist questions to draw out their in-depth knowledge and experience. The interview department can also analyze the specialist's past achievements and statements to create a customized list of questions. For example, the generation AI analyzes the specialist's past papers and books to extract important themes and topics. Step 2: The knowledge organizer organizes the knowledge and experience elicited by the interviewer. For example, the generative AI converts the interview content into text and edits it to make the specialized knowledge easier for the general public to understand. The knowledge organizer can also automatically tag the interview content and link it to other related content. For example, the generative AI analyzes the interview content and automatically tags important keywords and phrases. Step 3: The sharing department shares the knowledge and experience organized by the knowledge organization department. For example, the generation AI converts the interview content into video or audio format and publishes it on a media platform. The sharing department can also use the generation AI to analyze the specialist's past achievements and statements and create text that highlights the specialist's passion and efforts. For example, the generation AI analyzes the specialist's past achievements and statements and creates text that highlights the specialist's passion and efforts.

[0060] (Example 2) A media platform according to an embodiment of the present invention is a system for organizing, sharing, and spreading the knowledge and experience of specialists. In this system, a generative AI conducts interviews, extracts knowledge and experience, organizes it, and shares it. This allows the media platform to effectively organize, share, and spread the knowledge and experience of specialists.

[0061] A media platform according to an embodiment includes an interview unit, a knowledge organization unit, and a sharing unit. The interview unit interviews specialists to extract their knowledge and experience. For example, a generation AI asks questions to the specialists to extract their deep knowledge and experience. The interview unit can also analyze the specialists' past achievements and statements to create a customized list of questions. For example, the generation AI analyzes the specialists' past papers and books to extract important themes and topics. The knowledge organization unit organizes the knowledge and experience extracted by the interview unit. For example, the generation AI converts the interview content into text and edits it to make the specialized knowledge easier for the general public to understand. The knowledge organization unit can also automatically tag the interview content and link it to other related content. For example, the generation AI analyzes the interview content and automatically tags important keywords and phrases. The sharing unit shares the knowledge and experience organized by the knowledge organization unit. For example, the generation AI converts the interview content into video or audio format and publishes it on a media platform. The sharing unit can also use generative AI to analyze a specialist's past achievements and statements, and create text that highlights that person's passion and efforts. For example, generative AI can analyze a specialist's past achievements and statements and create text that highlights that person's passion and efforts. This allows the media platform to effectively organize, share, and spread the specialist's knowledge and experience.

[0062] The interview unit uses emotion estimation functionality to analyze changes in the specialist's emotions in real time and automatically generate questions that will heighten their emotions. For example, the generation AI in the interview unit analyzes the specialist's facial expressions and tone of voice during the interview to detect changes in emotions in real time. It identifies moments when emotions rise and automatically generates relevant probing questions at that time. The generation AI in the interview unit also monitors the specialist's heart rate and galvanic response during the interview to detect heightened emotions. At the moment when emotions reach their peak, it automatically generates questions to elicit deeper insights. The generation AI in the interview unit also analyzes the specialist's past interview and speech data to identify topics that will heighten their emotions. It automatically generates questions related to those topics during the interview to elicit heightened emotions. This makes it possible to extract deep insights based on the specialist's emotions.

[0063] The interview department can analyze the specialist's past achievements and statements before the interview and create a customized list of questions. For example, in the interview department, the generation AI analyzes the specialist's past papers and books to extract important themes and topics. Based on that information, it creates a customized list of questions to dig deeper in the interview. The interview department also analyzes the specialist's past lectures and interview videos to identify frequently mentioned keywords and phrases. Based on this, it generates a list of questions to elicit deeper insights. In the interview department, the generation AI analyzes the specialist's social media posts and blog articles to identify topics of interest and recent activities. Based on this, it creates a customized list of questions to dig deeper in the interview. This makes it possible to elicit deep insights based on the specialist's past achievements.

[0064] The interview section can automatically generate follow-up questions after the interview to further dig deeper into the specialist's knowledge. For example, after the interview is completed, the generation AI analyzes the interview content and identifies any unresolved questions or topics that require further digging. Based on this, follow-up questions are automatically generated and sent to the specialist. In addition, in the interview section, the generation AI summarizes the interview content and extracts important points and newly emerged questions. Based on this, follow-up questions are created to elicit even deeper insights. In addition, in the interview section, the generation AI analyzes the specialist's answers after the interview and automatically generates follow-up questions that request related additional information and specific examples. This allows for deeper digging into knowledge. This makes it possible to elicit even deeper insights after the interview.

[0065] The interview section can refer to the specialist's past interviews and lectures to generate questions from different perspectives. For example, in the interview section, the generation AI analyzes the specialist's past interview and lecture data to identify topics that have already been covered. Based on this, questions from different perspectives or new angles are automatically generated. The interview section also references the specialist's past statements and achievements to generate questions that dig deeper into existing knowledge. For example, questions are created that ask for rebuttals or clarification of past statements. The interview section also uses the generation AI to analyze the content of the specialist's past interviews and lectures to generate questions that address unresolved questions or provide new perspectives. This increases the depth of the interview. This allows questions from different perspectives to elicit more multifaceted insights.

[0066] The interview section translates the interview content in real time, allowing specialists from different language areas to be interviewed simultaneously. In the interview section, for example, the generation AI translates the interview content in real time, allowing specialists from different language areas to be interviewed simultaneously. For example, an interview conducted in English can be instantly translated into Japanese or French. The interview section also uses the generation AI to translate in real time during the interview, allowing specialists from different language areas to participate simultaneously. This makes it possible to conduct multilingual interviews. The interview section also uses the generation AI to translate the interview content in real time, allowing specialists from different language areas to be asked questions simultaneously. This enables knowledge sharing from an international perspective. This makes it possible to conduct interviews with specialists from different language areas simultaneously.

[0067] The interview unit can use the emotion estimation function to identify the moment when the specialist's emotions are strongest during the interview and generate content that emphasizes that part. In the interview unit, for example, the generation AI analyzes the specialist's emotions in real time during the interview and identifies the moment when emotions are strongest. It then generates video or text content that emphasizes that part. In the interview unit, the generation AI also monitors the specialist's emotional changes during the interview and identifies the moment when emotions reach their peak. It then creates content that highlights that part. In the interview unit, the generation AI also analyzes the specialist's emotions during the interview and identifies the moment when emotions are strongest. It then generates articles or videos that emphasize that part and provides them to viewers. In this way, content that emphasizes the moments when emotions are strongest can be generated, eliciting empathy from viewers.

[0068] The knowledge organization unit can build a knowledge network by automatically tagging interview content and linking it to other related content. For example, the knowledge organization unit uses a generation AI to analyze interview content and automatically tag important keywords and phrases. This links it to other related content and builds a knowledge network. The knowledge organization unit also builds a system that automatically tags interview content and links it to related articles and videos. For example, it associates it with other interviews and materials on the same topic. The knowledge organization unit also builds a knowledge network by using a generation AI to tag interview content and link it to other related content. This allows users to easily access related information. Building a knowledge network allows users to easily access related information.

[0069] The knowledge organization unit can organize the interview content in chronological order and visually display the evolution of the specialist's knowledge. In the knowledge organization unit, for example, the generation AI organizes the interview content in chronological order and visually displays the evolution of the specialist's knowledge. For example, important events and discoveries by year are displayed in timeline format. The knowledge organization unit also organizes the interview content in chronological order and builds a system that visually displays the evolution of the specialist's career and knowledge. For example, this is visualized using graphs and charts. In addition, the knowledge organization unit organizes the interview content in chronological order and visually displays the evolution of the specialist's knowledge. This allows the user to understand the specialist's growth process at a glance. By visually displaying the evolution of the specialist's knowledge, the growth process can be understood at a glance.

[0070] The knowledge organization unit can use the emotion estimation function to extract the parts of the interview content that evoke the most emotional empathy, and highlight and share them. For example, the knowledge organization unit uses the generation AI to analyze the interview content and use the emotion estimation function to extract the parts that evoke the most emotional empathy. The knowledge organization unit then creates and shares an article or video that highlights those parts. The knowledge organization unit also analyzes the interview content using the emotion estimation function to identify the parts that generate the most emotion. By highlighting and sharing those parts, it is possible to elicit empathy from viewers. The knowledge organization unit also uses the generation AI to analyze the interview content using the emotion estimation function to extract the parts that evoke the most emotional empathy. The knowledge organization unit then creates content that highlights those parts and shares it on a media platform. In this way, it is possible to elicit empathy from viewers by highlighting and sharing the parts that evoke the most emotional empathy.

[0071] The knowledge organization unit can convert interview content into different media formats and share it on multiple platforms. For example, the generation AI in the knowledge organization unit converts interview content into text, audio, or video formats and shares it on multiple platforms. For example, it can publish it as a blog post, podcast, or YouTube video. The knowledge organization unit can also build a system that converts interview content into different media formats and shares it on social media and media platforms. For example, it can distribute text as an article and audio as a podcast. The knowledge organization unit can also build a system that converts interview content into text, audio, or video formats and shares it on multiple platforms. This allows information to be delivered to different user groups. By sharing it in different media formats, information can be delivered to different user groups.

[0072] The knowledge organization unit can summarize the interview content and share it in a form that is easy to understand for specialists in different fields of expertise. For example, the knowledge organization unit uses a generation AI to summarize the interview content and share it in a form that is easy to understand for specialists in different fields of expertise. For example, technical terms may be replaced with simpler terms or diagrams may be used. The knowledge organization unit also builds a system in which the generation AI summarizes the interview content and shares it in a form that is easy to understand for specialists in different fields of expertise. For example, the main points may be organized in bullet points. The knowledge organization unit also uses a generation AI to summarize the interview content and share it in a form that is easy to understand for specialists in different fields of expertise. This promotes knowledge sharing across different fields. This makes it possible to share information in a form that is easy to understand for specialists in different fields.

[0073] The knowledge organization unit uses the emotion estimation function to analyze the user's emotional response to the interview content and can re-edit it in a format that will resonate with the most. For example, in the knowledge organization unit, the generation AI analyzes the interview content using the emotion estimation function and analyzes the user's emotional response. The knowledge organization unit re-edits it in a format that will resonate with the most and shares it as an article or video. The knowledge organization unit also builds a system that analyzes the user's emotional response to the interview content using the emotion estimation function and re-edits it in a format that will resonate with the most. For example, it emphasizes parts that are emotionally charged. The knowledge organization unit also builds a system in which the generation AI analyzes the interview content using the emotion estimation function and re-edits it in a format that will resonate with the most based on the user's emotional response. This creates content that elicits empathy from viewers. This allows the content to be re-edited in a format that will resonate with the most based on the user's emotional response.

[0074] The sharing unit uses a generative AI to analyze a specialist's past achievements and statements, and create text that highlights that person's passion and effort. For example, the sharing unit uses a generative AI to analyze a specialist's past achievements and statements, and create text that highlights that person's passion and effort. For example, it could highlight important projects and achievements. The sharing unit also uses a generative AI to analyze a specialist's past achievements and statements, and create text that highlights that person's passion and effort. This helps to increase the specialist's confidence. The sharing unit also uses a generative AI to analyze a specialist's past achievements and statements, and create text that highlights that person's passion and effort. For example, it could describe in detail the process of many years of research and development. This helps to increase the specialist's confidence by creating text that highlights their passion and effort.

[0075] The sharing unit can generate infographics that visually express the specialist's passion based on the interview content. In the sharing unit, for example, the generation AI generates infographics that visually express the specialist's passion based on the interview content. For example, research results or project progress may be shown in graphs and charts. In addition, the sharing unit also creates infographics that visually express the specialist's passion based on the interview content. This provides information in a visually easy-to-understand format. In addition, the sharing unit also has the generation AI analyze the interview content and generate infographics that visually express the specialist's passion. For example, important data and statistics may be visualized. In this way, by visually expressing the specialist's passion, information can be provided in an easy-to-understand format.

[0076] The sharing unit can use the emotion estimation function to identify the moment when the specialist is most confident and create content that emphasizes that part. For example, the sharing unit uses the emotion estimation function during an interview with the generation AI to identify the moment when the specialist is most confident. The sharing unit then creates video or text content that emphasizes that part. The sharing unit also analyzes the interview content with the emotion estimation function to identify the moment when the specialist is most confident. The sharing unit then creates an article or video that emphasizes that part. The sharing unit also uses the emotion estimation function to identify the moment when the specialist is most confident. The sharing unit then creates content that emphasizes that part and provides it to viewers. In this way, by emphasizing the moment when the specialist is most confident, it is possible to make a strong impression on viewers.

[0077] The sharing section can use the generative AI to compare the passion of a specialist with experts in other fields to identify commonalities and differences. For example, the generative AI can compare the passion of a specialist with experts in other fields to identify commonalities and differences. For example, it can compare the sources of passion and motivations in different fields. The sharing section can also compare the passion of a specialist with experts in other fields to identify commonalities and differences. This allows for the sharing of knowledge and experience in different fields. The generative AI can also compare the passion of a specialist with experts in other fields to identify commonalities and differences. For example, it can compare the success factors and challenges in different fields. This allows for the comparison with experts in different fields to identify commonalities and differences.

[0078] The sharing unit can create guidelines for other users to find their own passions based on the specialist's passions. In the sharing unit, for example, the generation AI creates guidelines for other users to find their own passions based on the specialist's passions. For example, it provides steps and hints for finding passions. In addition, the sharing unit has the generation AI analyze the specialist's passions and create guidelines for other users to find their own passions. This makes it easier for users to discover their own passions. In addition, the sharing unit has the generation AI create guidelines for other users to find their own passions based on the specialist's passions. For example, it shows how to choose and work in a field that you are passionate about. This makes it possible to provide guidelines for other users to find their own passions.

[0079] The sharing unit can use the emotion estimation function to identify the hobby for which the user feels the most positive emotion and generate content related to that hobby. For example, the sharing unit uses the emotion estimation function with a generation AI to identify the hobby for which the user feels the most positive emotion. The sharing unit generates content related to that hobby to attract the user's interest. The sharing unit also analyzes the user's emotional response with the emotion estimation function to identify the hobby for which the user feels the most positive emotion. The sharing unit generates content related to that hobby to increase the user's interest. The sharing unit also uses the emotion estimation function with a generation AI to identify the hobby for which the user feels the most positive emotion. The sharing unit generates content related to that hobby to expand the user's range of hobbies. In this way, the user's interest can be increased by generating content related to the hobby for which the user feels the most positive emotion.

[0080] The sharing unit uses a generation AI to analyze the user's past behavioral data and suggest the most relatable hobbies. For example, the sharing unit uses a generation AI to analyze the user's past behavioral data and suggest the most relatable hobbies. For example, it recommends hobbies based on past search history and browsing history. The sharing unit also uses a generation AI to analyze the user's past behavioral data and suggest the most relatable hobbies. This makes it easier for users to discover new hobbies. The sharing unit also uses a generation AI to analyze the user's past behavioral data and suggest the most relatable hobbies. For example, it recommends hobbies based on past purchase history and events attended. This makes it possible to suggest the most relatable hobbies based on the user's past behavioral data.

[0081] The sharing unit can generate a step-by-step guide for the user to start a new hobby based on the interview content. In the sharing unit, for example, the generation AI generates a step-by-step guide for the user to start a new hobby based on the interview content. For example, it provides detailed explanations of the necessary tools and first steps. In addition, the sharing unit creates a step-by-step guide for the user to start a new hobby based on the interview content. This allows the user to easily start a new hobby. In addition, the sharing unit analyzes the interview content and generates a step-by-step guide for the user to start a new hobby. For example, it provides advice and tips for beginners. This allows the user to be provided with specific guidelines for starting a new hobby.

[0082] The sharing section uses the generating AI to compare hobbies from different cultural spheres and provide the user with a new perspective. For example, the generating AI in the sharing section analyzes hobbies from different cultural spheres and provides the user with a new perspective. For example, it introduces traditional hobbies and activities from different countries or regions. The sharing section also uses the generating AI to compare hobbies from different cultural spheres and provides the user with a new perspective. This allows the user to become interested in hobbies from different cultures. The sharing section also uses the generating AI to analyze hobbies from different cultural spheres and provides the user with a new perspective. For example, it introduces the history and background of hobbies from different cultures. This allows the user to be provided with a new perspective by comparing hobbies from different cultural spheres.

[0083] The sharing unit can use the generation AI to automatically create a community for starting a new hobby based on the user's interests and encourage participation. For example, the generation AI automatically creates a community for starting a new hobby based on the user's interests. For example, it generates an online forum or a social networking group. The sharing unit can also use the generation AI to automatically create a community for users to start a new hobby and encourage participation. This allows users to interact with people who have the same hobby. The sharing unit can also use the generation AI to automatically create a community for starting a new hobby based on the user's interests and encourage participation. For example, it can suggest events or workshops related to the hobby. This allows it to automatically create a community for users to start a new hobby and encourage participation.

[0084] The sharing unit can use the emotion estimation function to identify the hobby for which the user feels the most positive emotion and generate content related to that hobby. For example, the sharing unit uses the emotion estimation function with a generation AI to identify the hobby for which the user feels the most positive emotion. The sharing unit generates content related to that hobby to attract the user's interest. The sharing unit also analyzes the user's emotional response with the emotion estimation function to identify the hobby for which the user feels the most positive emotion. The sharing unit generates content related to that hobby to increase the user's interest. The sharing unit also uses the emotion estimation function with a generation AI to identify the hobby for which the user feels the most positive emotion. The sharing unit generates content related to that hobby to expand the user's range of hobbies. In this way, the user's interest can be increased by generating content related to the hobby for which the user feels the most positive emotion.

[0085] The sharing department can use the generating AI to analyze the impact of the specialist's passion on the economy based on the interview content and provide specific data. For example, the sharing department uses the generating AI to analyze the interview content and analyze the impact of the specialist's passion on the economy. For example, it can provide data such as the market size and growth rate of a specific field. The sharing department can also use the generating AI to analyze the impact of the specialist's passion on the economy based on the interview content and provide specific data. This makes the economic impact visible. The sharing department can also use the generating AI to analyze the interview content and analyze the impact of the specialist's passion on the economy. For example, it can provide data such as job creation and investment amounts in related industries. This makes it possible to provide specific data on the impact of the specialist's passion on the economy.

[0086] The sharing unit can use the generation AI to predict demand for related products and services based on the interview content and create an economic development scenario. For example, the sharing unit has the generation AI analyze the interview content and predict demand for related products and services. For example, it predicts market demand for a new product based on specific technology or knowledge. The sharing unit also has the generation AI analyze the interview content and predict demand for related products and services and create an economic development scenario. This allows for specific proposals to companies and investors. The sharing unit also has the generation AI analyze the interview content and predict demand for related products and services. For example, it predicts market demand for products related to a new hobby or activity and creates an economic development scenario. This allows for demand forecasts for related products and services and create an economic development scenario.

[0087] The sharing unit can use the emotion estimation function to identify the economic impacts that users are most interested in and generate content that emphasizes those parts. For example, in the sharing unit, the generation AI uses the emotion estimation function to identify the economic impacts that users are most interested in. It then creates articles and videos that emphasize those parts and provides them to viewers. The sharing unit also analyzes interview content with the emotion estimation function to identify the economic impacts that users are most interested in. It then generates content that emphasizes those parts, thereby attracting viewer interest. The sharing unit also uses the emotion estimation function to identify the economic impacts that users are most interested in. It then creates articles and videos that emphasize those parts and provides them to viewers. This makes it possible to generate content that emphasizes the economic impacts that users are most interested in.

[0088] The sharing unit uses the generative AI to compare the passions of specialists in different fields and identify common elements that contribute to economic development. For example, the generative AI analyzes the passions of specialists in different fields and identifies common elements that contribute to economic development. For example, it extracts elements of innovation and creativity. The sharing unit also uses the generative AI to compare the passions of specialists in different fields and identify common elements that contribute to economic development. This allows knowledge and experience from different fields to be shared. The generative AI also analyzes the passions of specialists in different fields and identifies common elements that contribute to economic development. For example, it extracts elements of sustainable development and social impact. This allows common elements that contribute to economic development to be identified by comparing the passions of specialists in different fields.

[0089] The sharing department can use generative AI to propose new business models that contribute to economic development based on the interview content. For example, the sharing department will use generative AI to analyze the interview content and propose new business models that contribute to economic development. For example, it will propose new services and products that utilize the knowledge and experience of specialists. The sharing department will also use generative AI to propose new business models that contribute to economic development based on the interview content. This allows for specific proposals to be made to companies and investors. The sharing department will also use generative AI to analyze the interview content and propose new business models that contribute to economic development. For example, it will propose new markets and business opportunities that utilize the passion of specialists. This allows for the proposal of new business models that contribute to economic development.

[0090] The sharing unit can use the emotion estimation function to identify the economic impact that users feel most positive about, and generate content that emphasizes that impact. For example, in the sharing unit, the generation AI uses the emotion estimation function to identify the economic impact that users feel most positive about. It then creates articles and videos that emphasize that impact and provides them to viewers. The sharing unit also analyzes interview content with the emotion estimation function to identify the economic impact that users feel most positive about. It then generates content that emphasizes that impact, thereby attracting viewer interest. The sharing unit also uses the emotion estimation function to identify the economic impact that users feel most positive about. It then creates articles and videos that emphasize that impact and provides them to viewers. This makes it possible to generate content that emphasizes the economic impact that users feel most positive about.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The interview department can also introduce interactive quiz-style questions to draw out specialists' knowledge and experience. For example, a generative AI can ask quiz-style questions to specialists and customize the next questions based on their answers. The interview department can also generate quizzes to test specialists' knowledge and adjust the direction of the interview based on the results. Furthermore, the interview department can analyze the specialists' responses when answering quizzes to identify topics that they are particularly interested in. This makes interviews more interactive and effective.

[0093] The interview unit uses emotion estimation functionality to analyze changes in the specialist's emotions in real time and automatically generate questions that will heighten their emotions. For example, the generation AI analyzes the specialist's facial expressions and tone of voice during the interview to detect changes in emotions in real time. It identifies moments when emotions rise and automatically generates relevant, probing questions at that time. The interview unit also monitors the specialist's heart rate and galvanic response during the interview to detect heightened emotions. At the moment when emotions peak, it automatically generates questions to elicit deeper insights. The interview unit also analyzes the specialist's past interview and speech data to identify topics that will heighten their emotions. It automatically generates questions related to those topics during the interview to elicit heightened emotions. This makes it possible to extract deep insights based on the specialist's emotions.

[0094] The interview department can analyze the specialist's past achievements and statements before the interview to create a customized list of questions. For example, the generative AI analyzes the specialist's past papers and books to extract important themes and topics. Based on that information, it creates a customized list of questions to dig deeper during the interview. The interview department can also analyze the specialist's past lectures and interview videos to identify frequently mentioned keywords and phrases. Based on this, it generates a list of questions to elicit deeper insights. The interview department can also use the generative AI to analyze the specialist's social media posts and blog articles to identify topics of interest and recent activities. Based on this, it creates a customized list of questions to dig deeper during the interview. This makes it possible to elicit deep insights based on the specialist's past achievements.

[0095] The interview department can automatically generate follow-up questions after the interview to further dig deeper into the specialist's knowledge. For example, after the interview is completed, the generation AI analyzes the interview content and identifies any unresolved questions or topics that require further digging. Based on this, follow-up questions are automatically generated and sent to the specialist. In addition, in the interview department, the generation AI summarizes the interview content and extracts important points and newly emerged questions. Based on this, follow-up questions are created to elicit even deeper insights. In addition, in the interview department, the generation AI analyzes the specialist's answers after the interview and automatically generates follow-up questions that request related additional information and specific examples. This allows for deeper digging into knowledge. This makes it possible to elicit even deeper insights after the interview.

[0096] The interview section can refer to the specialist's past interviews and lectures to generate questions from different perspectives. For example, the generation AI analyzes the specialist's past interview and lecture data to identify topics that have already been covered. Based on this, questions from different perspectives or new angles are automatically generated. The interview section also references the specialist's past statements and achievements to generate questions that dig deeper into existing knowledge. For example, it creates questions that ask for rebuttals or clarification of past statements. The interview section also uses the generation AI to analyze the content of the specialist's past interviews and lectures to generate questions that address unresolved questions or provide new perspectives. This increases the depth of the interview. This allows questions from different perspectives to elicit more multifaceted insights.

[0097] The interview section translates the interview content in real time, allowing specialists from different language areas to be interviewed simultaneously. For example, the generation AI translates the interview content in real time, allowing specialists from different language areas to be interviewed simultaneously. For example, an interview conducted in English can be instantly translated into Japanese or French. The interview section also uses the generation AI to translate in real time during the interview, allowing specialists from different language areas to participate simultaneously. This makes multilingual interviews possible. The interview section also uses the generation AI to translate the interview content in real time, allowing specialists from different language areas to be asked questions simultaneously. This enables knowledge sharing from an international perspective. This makes it possible to interview specialists from different language areas simultaneously.

[0098] The interview department can use the emotion estimation function to identify the moment when the specialist's emotions are strongest during the interview and generate content that emphasizes that part. For example, the generation AI analyzes the specialist's emotions in real time during the interview and identifies the moment when emotions are strongest. It then generates video or text content that emphasizes that part. The interview department also monitors the specialist's emotional changes during the interview and identifies the moment when emotions reach their peak. It then creates content that highlights that part. The interview department also uses the generation AI to analyze the specialist's emotions during the interview and identify the moment when emotions are strongest. It then generates articles or videos that emphasize that part and provides them to viewers. This generates content that emphasizes the moments when emotions are strongest, thereby eliciting empathy from viewers.

[0099] The knowledge organization unit can build a knowledge network by automatically tagging interview content and linking it to other related content. For example, the generation AI analyzes interview content and automatically tags important keywords and phrases. This links it to other related content and builds a knowledge network. The knowledge organization unit also builds a system that automatically tags interview content and links it to related articles and videos. For example, it associates it with other interviews and materials on the same topic. The knowledge organization unit also builds a knowledge network by having the generation AI tag interview content and link it to other related content. This allows users to easily access related information. Building a knowledge network allows users to easily access related information.

[0100] The knowledge organization unit can organize the interview content in chronological order and visually display the evolution of the specialist's knowledge. For example, the generation AI organizes the interview content in chronological order and visually displays the evolution of the specialist's knowledge. For example, important events and discoveries by year are displayed in timeline format. The knowledge organization unit also organizes the interview content in chronological order and builds a system that visually displays the evolution of the specialist's career and knowledge. For example, this is visualized using graphs and charts. The knowledge organization unit also organizes the interview content in chronological order and visually displays the evolution of the specialist's knowledge. This allows the user to understand the specialist's growth process at a glance. By visually displaying the evolution of the specialist's knowledge, the growth process can be understood at a glance.

[0101] The knowledge organization unit can use the emotion estimation function to extract the parts of the interview content that evoke the most emotional empathy, and then highlight and share them. For example, the generation AI analyzes the interview content and uses the emotion estimation function to extract the parts that evoke the most emotional empathy. An article or video that highlights those parts is created and shared. The knowledge organization unit also analyzes the interview content using the emotion estimation function to identify the parts that evoke the most emotion. By highlighting and sharing those parts, it is possible to elicit empathy from viewers. The knowledge organization unit also analyzes the interview content using the emotion estimation function to extract the parts that evoke the most emotional empathy. By highlighting and sharing those parts, it is possible to elicit empathy from viewers.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The interview department interviews the specialist to draw out their knowledge and experience. For example, the generation AI asks the specialist questions to draw out their in-depth knowledge and experience. The interview department can also analyze the specialist's past achievements and statements to create a customized list of questions. For example, the generation AI analyzes the specialist's past papers and books to extract important themes and topics. Step 2: The knowledge organizer organizes the knowledge and experience elicited by the interviewer. For example, the generative AI converts the interview content into text and edits it to make the specialized knowledge easier for the general public to understand. The knowledge organizer can also automatically tag the interview content and link it to other related content. For example, the generative AI analyzes the interview content and automatically tags important keywords and phrases. Step 3: The sharing department shares the knowledge and experience organized by the knowledge organization department. For example, the generation AI converts the interview content into video or audio format and publishes it on a media platform. The sharing department can also use the generation AI to analyze the specialist's past achievements and statements and create text that highlights the specialist's passion and efforts. For example, the generation AI analyzes the specialist's past achievements and statements and creates text that highlights the specialist's passion and efforts.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The interview department interviews specialists and draws out their knowledge and experience. a knowledge organizing unit that organizes the knowledge and experience elicited by the interview unit; a sharing unit that shares the knowledge and experience organized by the knowledge organizing unit. A system characterized by:

2. The interview section Analyzing changes in the specialist's emotions in real time and automatically generating questions that increase the emotions 2. The system of claim 1.

3. The knowledge organizing unit Build a network of knowledge by automatically tagging interview content and linking it to other related content 2. The system of claim 1.

4. The common part is Using generative AI to analyze the specialist's past achievements and statements, we create text that highlights their passion and efforts.

2. The system of claim 1.

5. The common part is Identify the hobbies that interest you most and provide detailed information about those hobbies 2. The system of claim 1.

Images